Advancing Patient Matching to Promote Interoperability

Session 18, February 12, 2019

Rita Torkzadeh, The Pew Charitable Trusts, @ritorkzadeh

Ben Moscovitch, The Pew Charitable Trusts, @benmoscovitch

Rita Torkzadeh, MS

Ben Moscovitch, MA

Have no real or apparent conflicts of interest to report.

Conflict of Interest

• Current state of patient matching

• Findings from Pew’s research

– Unique identifiers & biometrics

– Patient-involvement

– Demographic data standardization

– Referential matching

• Short and long-term recommendations

• Facilitated discussion

Agenda

• Differentiate between the various ways hospitals currently match

patients and why the current state is insufficient

• Describe a shared vision for a coordinated nationwide strategy to

advance matching based on recently published work from multiple

groups and perspectives

• Assess research findings from focus groups with patients,

interviews with providers, and others on alternatives to improve

matching that include the use of unique identifiers, biometrics,

patient involvement, data standardization and using third party

data

• Identify recommendations based on research findings for

advancing matching in the near-term and long-term to make

nationwide progress

Learning Objectives

Matching is not identification.

Identification is:

Matching—what is it

This is Dave Robbins.

I know because of information

he gave me: his driver’s

license or a palm scan, etc.

Matching is finding the right record.

Hospital

Hospital B

Dave’s

EHR

Dave’s

EHR

Dave’s

EHR

Dave’s

EHR

Matching: Current Implications

Matching: Implications

Matching: Common Problems

Denver

Nashville

Houston

Richmond

Philadelphi

• Literature reviews

• Conversations with experts

• Contracted research

• Focus groups with patients

• Interviews with health executives

Research Conducted

• Generally unaware; some personal experience

• Strong support for improvements

– timely access to health data

– quicker/comprehensive care

– Avoiding unnecessary procedures and costs

• Modest differences among groups

Focus Group Patient Views

• Internal versus external matching

• Match goal > 99 percent

• Investments: 0.5-9 FTEs

• Diminishing returns

Interviewed Health Exec Views

Needs work

• Prohibition on HHS finalizing one

• May be more advanced solutions

• Patients: preference for unique IDs

– Biometrics preferred

• Providers:

– Biometrics could help in emergencies

• Technical and costs barriers:

– Biometric template challenges/privacy

– Concerns around implementation, adoption, and expense

Opportunity 1: Unique IDs

• Commissioned RAND report

1. Phone number verification

2. Smartphone app

3. Additional features

• Virtual clipboard

• Built into PHRs

• Mixed views from patient and providers

– Patients liked reduced paperwork, questioned widespread adoption

– Providers: benefit for patient engagement, adoption/security concerns

Opportunity 2: Patient-led

• Been recommended for years

– Tested it with Indiana University

– Focus groups supported uniform data capture across providers

– Providers interviewed unanimously agreed this is essential

Opportunity 3: Standardization

• Use of third-party data sources

• Among the highest reported match rates

• Some limitations & concerns among focus groups and providers

Opportunity 4: Referential

1. Congress clarify funding

2. Verification of data

3. Assess privacy

4. Examine referential matching

5. Agreement on data standards & elements

– Standards (e.g. for address)

– Exchange collected data (e.g. email)

Near-term opportunities

1. Establish trusted entity

– Recognized Coordinating Entity/TEFCA

2. Leverage smartphones

3. Identify infrastructure

– Standards and infrastructure that includes biometrics for

inter-organization matching

– Privacy and security

Long-term opportunities

• Progress on inter-organization matching is essential

– Patients want and expect it

– Providers need it

• Standards are our lodestar

– Demographic data

– Future technology—biometrics

Conclusions

Quality

Costs

1. What experience with record matching failures (or

successes) do you have? How do you measure

match rates?

2. Which solutions do you think have the most

promise, and why? What could be done to move

them forward?

3. Standardization of data showed clear benefits.

What do organizations today need to standardize

data?

4. Biometrics is part of many industries’ identity

solutions. What are your perspectives on the role

that biometrics can and should play in healthcare?

What features would need to exist for adoption

and to ensure privacy?

Discussion

Ben Moscovitch

Project Director, Health Information Technology

The Pew Charitable Trusts

e: bmoscovitch@pewtrusts.org

@benmoscovitch

Rita Torkzadeh

Officer, Health Information Technology

The Pew Charitable Trusts

e: rtorkzadeh@pewtrusts.org

@ritorkzadeh

Questions